Analysing Seasonal Health Data
(Sprache: Englisch)
This book explains statistical methods for finding and estimating seasonal patterns. It describes methods for a range of outcomes (including continuous, count and binomial data) and demonstrates appropriate techniques for summarizing and modeling these data.
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Produktinformationen zu „Analysing Seasonal Health Data “
This book explains statistical methods for finding and estimating seasonal patterns. It describes methods for a range of outcomes (including continuous, count and binomial data) and demonstrates appropriate techniques for summarizing and modeling these data.
Klappentext zu „Analysing Seasonal Health Data “
Seasonal patterns have been found in a remarkable range of health conditions, including birth defects, respiratory infections and cardiovascular disease. Accurately estimating the size and timing of seasonal peaks in disease incidence is an aid to understanding the causes and possibly to developing interventions. With global warming increasing the intensity of seasonal weather patterns around the world, a review of the methods for estimating seasonal effects on health is timely.This is the first book on statistical methods for seasonal data written for a health audience. It describes methods for a range of outcomes (including continuous, count and binomial data) and demonstrates appropriate techniques for summarising and modelling these data. It has a practical focus and uses interesting examples to motivate and illustrate the methods. The statistical procedures and example data sets are available in an R package called 'season'.
Inhaltsverzeichnis zu „Analysing Seasonal Health Data “
to Seasonality.- Cosinor.- Decomposing Time Series.- Controlling for Season.- Clustered Seasonal Data.
Autoren-Porträt von Adrian G. Barnett, Annette J. Dobson
Adrian Barnett is a senior research fellow at Queensland University of Technology, Australia. Annette Dobson is a Professor of Biostatistics at The University of Queensland, Australia. Both are experienced medical statisticians with a commitment to statistical education and have previously collaborated in research in the methodological developments and applications of biostatistics, especially to time series data. Among other projects, they worked together on revising the well-known textbook "An Introduction to Generalized Linear Models," third edition, Chapman Hall/CRC, 2008. In their new book they share their knowledge of statistical methods for examining seasonal patterns in health.
Bibliographische Angaben
- Autoren: Adrian G. Barnett , Annette J. Dobson
- 2010, XIII, 164 Seiten, Maße: 16,4 x 24,2 cm, Gebunden, Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3642107478
- ISBN-13: 9783642107474
- Erscheinungsdatum: 26.02.2010
Sprache:
Englisch
Rezension zu „Analysing Seasonal Health Data “
From the reviews:"This book is aimed at both non-statistical researchers and statisticians, and it is presented as 'the first book on statistical methods for seasonal data for a health audience'. ... this is a useful book on an important subject and I would recommend it to anybody interested in the analysis of seasonal data." (Mario Cortina Borja, Significance, June, 2011)
"The authors are to be commended on a useful and clear introduction to seasonal health data analysis. The text will be helpful to statisticians, particularly in combination with the associated R package 'season', which will encourage them to test their own preferred methods in context and assist in teaching seasonal modelling." (Malcolm Hudson, Australian & New Zealand Journal of Statistics, Vol. 53 (3), 2011)
Pressezitat
From the reviews:"This book is aimed at both non-statistical researchers and statisticians, and it is presented as 'the first book on statistical methods for seasonal data for a health audience'. ... this is a useful book on an important subject and I would recommend it to anybody interested in the analysis of seasonal data." (Mario Cortina Borja, Significance, June, 2011)
"The authors are to be commended on a useful and clear introduction to seasonal health data analysis. The text will be helpful to statisticians, particularly in combination with the associated R package 'season', which will encourage them to test their own preferred methods in context and assist in teaching seasonal modelling." (Malcolm Hudson, Australian & New Zealand Journal of Statistics, Vol. 53 (3), 2011)
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